New College Grad - Computational Physics & Agentic AI Engineer, Doctorate Degree (Spring 2027)

Applied Materials • Santa Clara, CA

5d

About The Position

Applied Materials is hiring an early-career PhD-level scientist or engineer to join a computational modeling team developing and applying physics-based simulation tools and digital twins for semiconductor process chambers and plasma processing applications. The role focuses on improving understanding and prediction of thermal and plasma-driven processes—including deposition, etching, plasma-enhanced processing, and plasma-surface interactions—and making modeling capabilities more accessible to engineers through AI-driven software and workflow tools.

The engineer will work across computational physics, high-performance computing, and AI/ML to support technology and product development in semiconductor equipment. The work includes developing multi-scale and multi-fidelity modeling capabilities, linking high-fidelity solvers with reduced-order and surrogate approaches, and establishing verification and validation methods for computational models and AI-enabled workflows. Applications are reviewed on a rolling basis. The posting asks candidates to apply by November 20, 2026, and notes that the position may close earlier based on application volume or candidate selection.

Requirements

• MS or PhD in Mechanical Engineering, Electrical Engineering, Aerospace Engineering, Chemical Engineering, Materials Science, Physics, or a related field.
• Demonstrated computational physics or multiphysics modeling experience in one or more areas such as transport phenomena, fluid dynamics, heat transfer, chemistry, thermal-structural mechanics, plasma dynamics, or feature-scale process modeling.
• Strong background in applied mathematics and computational methods, including numerical methods, statistical modeling, uncertainty quantification, or computational solver development.
• Strong programming experience in Python and/or C++, including experience developing scientific or engineering applications.
• Experience with machine learning for scientific or engineering applications, such as surrogate modeling, physics-informed AI, neural operators, or related data-driven approaches.
• Experience working with simulation data and computational models to analyze complex physical or engineering systems.
• Strong technical judgment and attention to detail.
• Excellent written and verbal communication skills.

Nice To Haves

• PhD degree.
• Experience with high-performance computing, parallel computing, or GPU acceleration for scientific and engineering applications.
• Experience with LLM-based agents, tool use, or agentic workflow orchestration for technical or scientific applications.
• Experience with multi-fidelity modeling, reduced-order modeling, surrogate modeling, or methods connecting models across different levels of fidelity.
• Familiarity with commercial, open-source, or in-house multiphysics simulation platforms such as ACE+, COMSOL, ANSYS Fluent, OpenFOAM, or similar tools.
• Experience developing scientific software, reusable modeling frameworks, engineering workflow automation, or digital twins.
• Experience applying computational modeling to semiconductor process or equipment applications, such as thermal processing, plasma processing, deposition, etching, or related multiphysics problems.

Responsibilities

• Apply computational modeling to semiconductor process and equipment development challenges, including thermal, plasma, deposition, etch, and related multiphysics applications.
• Develop, implement, and improve physics-based simulation capabilities, numerical methods, and engineering workflows.
• Use simulation, experimental, and engineering data to analyze process and hardware behavior, identify physical mechanisms, and support design decisions.
• Develop and evaluate reduced-order, surrogate, and machine-learning models where they improve computational efficiency or predictive capability.
• Build AI-enabled tools and agentic workflows to help engineers set up, execute, analyze, and interpret computational models.
• Collaborate with process, hardware, experimental, and software teams to define modeling problems, interpret results, and translate insights into engineering decisions.
• Verify and validate modeling and AI-enabled capabilities using reference solutions, simulation benchmarks, and experimental data.
• Contribute to internal modeling software, reusable workflows, documentation, and best practices.
• Communicate modeling results, assumptions, limitations, and recommendations to scientists, engineers, technical leadership, and customers.

Benefits

• Comprehensive benefits package.
• Candidates may be eligible for participation in a bonus and stock award program, as applicable.
• Supportive work culture that encourages learning, development, and career growth.
• Programs and support focused on employee health, wellbeing, and personal and professional growth.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level